The need for screening, early diagnosis, and prediction of chronic kidney disease in people with diabetes in low- and middle-income countries—a review of the current literature
Bibliographic record
Abstract
Chronic kidney disease (CKD) in people with diabetes is becoming an increasing major public health concern, disproportionately burdening low- and middle-income countries (LMICs). This rising burden is due to various factors, including the lack of disease awareness that results in late referral and the cost of screening and consequent treatment of the comorbid conditions, as well as other factors endemic to LMICs relating to inadequate management of risk factors. We critically assessed the extant literature, by performing searches of Medline via PubMed, EBSCOhost, Scopus, and Web of Science, for studies pertaining to screening, diagnosis, and prediction of CKD amongst adults with diabetes in LMICs, using relevant key terms. The relevant studies were summarized through key themes derived from the Wilson and Jungner criteria. We found that screening for CKD in people with diabetes is generally infrequent in LMICs. Also, LMICs are ill-equipped to appropriately manage diabetes-associated CKD, especially its late stages, in which supportive care and kidney replacement therapy (KRT) might be required. There are acceptable and relatively simple tools that can aid diabetes-associated CKD screening in these countries; however, these tools come with limitations. Thus, effective implementation of diabetes-associated CKD screening in LMICs remains a challenge, and the cost-effectiveness of such an undertaking largely remains to be explored. In conclusion, for many compelling reasons, screening for CKD in people with diabetes should be a high policy priority in LMICs, as the huge cost associated with higher mortality and morbidity in this group and the cost of KRT offers a compelling economic incentive for improving early detection of diabetes in CKD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".